SoroushVahidi/scidocs huggingface dataset:
Prevention of falls and consequent injuries in elderly people
(2005)

Injuries resulting from falls in elderly people are a major public-health concern, representing one of the main causes of longstanding pain, functional impairment, disability, and death in this population. The problem is going to worsen, since the rates of such injuries seem to be rising in many areas, as is the number of elderly people in both the developed and developing world. Many methods and programmes to prevent such injuries already exist, including regular exercise, vitamin D and calcium supplementation, withdrawal of psychotropic medication, cataract surgery, professional environment hazard assessment and modification, hip protectors, and multifactorial preventive programmes for simultaneous assessment and reduction of many of the predisposing and situational risk factors. To receive broader-scale effectiveness, these programmes will need systematic implementation. Care must be taken, however, to rigorously select the right actions for those people most likely to benefit, such as vitamin D and calcium supplementation and hip protectors for elderly people living in institutions.
Geometric Scene Parsing with Hierarchical LSTM
(2016)

This paper addresses the problem of geometric scene parsing, i.e. simultaneously labeling geometric surfaces (e.g. sky, ground and vertical plane) and determining the interaction relations (e.g. layering, supporting, siding and affinity) between main regions. This problem is more challenging than the traditional semantic scene labeling, as recovering geometric structures necessarily requires the rich and diverse contextual information. To achieve these goals, we propose a novel recurrent neural network model, named Hierarchical Long Short-Term Memory (H-LSTM). It contains two coupled sub-networks: the Pixel LSTM (P-LSTM) and the Multi-scale Super-pixel LSTM (MS-LSTM) for handling the surface labeling and relation prediction, respectively. The two sub-networks provide complementary information to each other to exploit hierarchical scene contexts, and they are jointly optimized for boosting the performance. Our extensive experiments show that our model is capable of parsing scene geometric structures and outperforming several state-of-theart methods by large margins. In addition, we show promising 3D reconstruction results from the still images based on the geometric parsing.
Securing wireless sensor networks: a survey
(2008)

The significant advances of hardware manufacturing technology and the development of efficient software algorithms make technically and economically feasible a network composed of numerous, small, low-cost sensors using wireless communications, that is, a wireless sensor network. WSNs have attracted intensive interest from both academia and industry due to their wide application in civil and military scenarios. In hostile scenarios, it is very important to protect WSNs from malicious attacks. Due to various resource limitations and the salient features of a wireless sensor network, the security design for such networks is significantly challenging. In this article, we present a comprehensive survey of WSN security issues that were investigated by researchers in recent years and that shed light on future directions for WSN security.
A theory of self-calibration of a moving camera
(1992)

There is a close connection between the calibration of a single camera and the epipolar transformation obtained when the camera undergoes a displacement. The epipolar transformation imposes two algebraic constraints on the camera calibration. If two epipolar transformations, arising from different camera displacements, are available then the compatible camera calibrations are parameterized by an algebraic curve of genus four. The curve can be represented either by a space curve of degree seven contained in the intersection of two cubic surfaces, or by a curve of degree six in the dual of the image plane. The curve in the dual plane has one singular point of order three and three singular points of order two. If three epipolar transformations are available, then two curves of degree six can be obtained in the dual plane such that one of the real intersections of the two yields the correct camera calibration. The two curves have a common singular point of order three. Experimental results are given to demonstrate the feasibility of camera calibration based on the epipolar transformation. The real intersections of the two dual curves are found by locating the zeros of a function defined on the interval [0, 2π]. The intersection yielding the correct camera calibration is picked out by referring back to the three epipolar transformations.
Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks
(2013)

Bearing degradation is the most common source of faults in electrical machines. In this context, this work presents a novel monitoring scheme applied to diagnose bearing faults. Apart from detecting local defects, i.e., single-point ball and raceway faults, it takes also into account the detection of distributed defects, such as roughness. The development of diagnosis methodologies considering both kinds of bearing faults is, nowadays, subject of concern in fault diagnosis of electrical machines. First, the method analyzes the most significant statistical-time features calculated from vibration signal. Then, it uses a variant of the curvilinear component analysis, a nonlinear manifold learning technique, for compression and visualization of the feature behavior. It allows interpreting the underlying physical phenomenon. This technique has demonstrated to be a very powerful and promising tool in the diagnosis area. Finally, a hierarchical neural network structure is used to perform the classification stage. The effectiveness of this condition-monitoring scheme has been verified by experimental results obtained from different operating conditions.
A delay-tolerant network architecture for challenged internets
(2003)

The highly successful architecture and protocols of today's Internet may operate poorly in environments characterized by very long delay paths and frequent network partitions. These problems are exacerbated by end nodes with limited power or memory resources. Often deployed in mobile and extreme environments lacking continuous connectivity, many such networks have their own specialized protocols, and do not utilize IP. To achieve interoperability between them, we propose a network architecture and application interface structured around optionally-reliable asynchronous message forwarding, with limited expectations of end-to-end connectivity and node resources. The architecture operates as an overlay above the transport layers of the networks it interconnects, and provides key services such as in-network data storage and retransmission, interoperable naming, authenticated forwarding and a coarse-grained class of service.
Gimli: open source and high-performance biomedical name recognition
(2012)

Automatic recognition of biomedical names is an essential task in biomedical information extraction, presenting several complex and unsolved challenges. In recent years, various solutions have been implemented to tackle this problem. However, limitations regarding system characteristics, customization and usability still hinder their wider application outside text mining research. We present Gimli, an open-source, state-of-the-art tool for automatic recognition of biomedical names. Gimli includes an extended set of implemented and user-selectable features, such as orthographic, morphological, linguistic-based, conjunctions and dictionary-based. A simple and fast method to combine different trained models is also provided. Gimli achieves an F-measure of 87.17% on GENETAG and 72.23% on JNLPBA corpus, significantly outperforming existing open-source solutions. Gimli is an off-the-shelf, ready to use tool for named-entity recognition, providing trained and optimized models for recognition of biomedical entities from scientific text. It can be used as a command line tool, offering full functionality, including training of new models and customization of the feature set and model parameters through a configuration file. Advanced users can integrate Gimli in their text mining workflows through the provided library, and extend or adapt its functionalities. Based on the underlying system characteristics and functionality, both for final users and developers, and on the reported performance results, we believe that Gimli is a state-of-the-art solution for biomedical NER, contributing to faster and better research in the field. Gimli is freely available at http://bioinformatics.ua.pt/gimli .
Using audio and visual information for single channel speaker separation
(2015)

This work proposes a method to exploit both audio and visual speech information to extract a target speaker from a mixture of competing speakers. The work begins by taking an effective audio-only method of speaker separation, namely the soft mask method, and modifying its operation to allow visual speech information to improve the separation process. The audio input is taken from a single channel and includes the mixture of speakers, where as a separate set of visual features are extracted from each speaker. This allows modification of the separation process to include not only the audio speech but also visual speech from each speaker in the mixture. Experimental results are presented that compare the proposed audio-visual speaker separation with audio-only and visual-only methods using both speech quality and speech intelligibility metrics.
Anchors aweigh: A demonstration of cross-modality anchoring and magnitude priming
(2008)

Research has shown that judgments tend to assimilate to irrelevant "anchors." We extend anchoring effects to show that anchors can even operate across modalities by, apparently, priming a general sense of magnitude that is not moored to any unit or scale. An initial study showed that participants drawing long "anchor" lines made higher numerical estimates of target lengths than did those drawing shorter lines. We then replicated this finding, showing that a similar pattern was obtained even when the target estimates were not in the dimension of length. A third study showed that an anchor's length relative to its context, and not its absolute length, is the key to predicting the anchor's impact on judgments. A final study demonstrated that magnitude priming (priming a sense of largeness or smallness) is a plausible mechanism underlying the reported effects. We conclude that the boundary conditions of anchoring effects may be much looser than previously thought, with anchors operating across modalities and dimensions to bias judgment.
An OpenCCG-Based Approach to Question Generation from Concepts
(2013)

Dialogue systems are often regarded as being tedious and inflexible. We believe that one reason is rigid and inadaptable system utterances. A good dialogue system should automatically choose a formulation that reflects the user’s expectations. However, current dialogue system development environments only allow the definition of questions with unchangeable formulations. In this paper we present a new approach to the generation of system questions by only defining basic concepts. This is the basis for realising adaptive, user-tailored, and human-like system questions in dialogue systems.
Toward negotiable reinforcement learning: shifting priorities in Pareto optimal sequential decision-making
(2017)

Existing multi-objective reinforcement learning (MORL) algorithms do not account for objectives that arise from players with differing beliefs. Concretely, consider two players with different beliefs and utility functions who may cooperate to build a machine that takes actions on their behalf. A representation is needed for how much the machine’s policy will prioritize each player’s interests over time. Assuming the players have reached common knowledge of their situation, this paper derives a recursion that any Pareto optimal policy must satisfy. Two qualitative observations can be made from the recursion: the machine must (1) use each player’s own beliefs in evaluating how well an action will serve that player’s utility function, and (2) shift the relative priority it assigns to each player’s expected utilities over time, by a factor proportional to how well that player’s beliefs predict the machine’s inputs. Observation (2) represents a substantial divergence from näıve linear utility aggregation (as in Harsanyi’s utilitarian theorem, and existing MORL algorithms), which is shown here to be inadequate for Pareto optimal sequential decision-making on behalf of players with different beliefs.
Facilitating Object Detection and Recognition through Eye Gaze
(2017)

When compared to image recognition, object detection is a much more challenging task because it requires the accurate real-time localization of an object in the target image. In interaction scenarios, this pipeline can be simplified by incorporating the users’ point of regard. Wearable eye trackers can estimate the gaze direction, but lack own processing capabilities. We enable mobile gaze-aware applications by developing an open-source platform which supports mobile eye tracking based on the Pupil headset and a smartphone running Android OS. Through our platform, we offer researchers and developers a rapid prototyping environment for gaze-enabled applications. We describe the concept, our current progress, and research implications. ACM Classification
DataVizard: Recommending Visual Presentations for Structured Data
(2018)

Selecting the appropriate visual presentation of the data such that it not only preserves the semantics but also provides an intuitive summary of the data is an important, often the final step of data analytics. Unfortunately, this is also a step involving significant human effort starting from selection of groups of columns in the structured results from analytics stages, to the selection of right visualization by experimenting with various alternatives. In this paper, we describe our DataVizard system aimed at reducing this overhead by automatically recommending the most appropriate visual presentation for the structured result. Specifically, we consider the following two scenarios: first, when one needs to visualize the results of a structured query such as SQL; and the second, when one has acquired a data table with an associated short description (e.g., tables from the Web). Using a corpus of real-world database queries (and their results) and a number of statistical tables crawled from the Web, we show that DataVizard is capable of recommending visual presentations with high accuracy.
Memorability of natural scenes: The role of attention
(2013)

The image memorability consists in the faculty of an image to be recalled after a period of time. Recently, the memorability of an image database was measured and some factors responsible for this memorability were highlighted. In this paper, we investigate the role of visual attention in image memorability around two axis. The first one is experimental and uses results of eye-tracking performed on a set of images of different memorability scores. The second investigation axis is predictive and we show that attention-related features can advantageously replace low-level features in image memorability prediction. From our work it appears that the role of visual attention is important and should be more taken into account along with other low-level features.
Real-time garbage collection for flash-memory storage systems of real-time embedded systems
(2004)

Flash-memory technology is becoming critical in building embedded systems applications because of its shock-resistant, power economic, and nonvolatile nature. With the recent technology breakthroughs in both capacity and reliability, flash-memory storage systems are now very popular in many types of embedded systems. However, because flash memory is a write-once and bulk-erase medium, we need a translation layer and a garbage-collection mechanism to provide applications a transparent storage service. In the past work, various techniques were introduced to improve the garbage-collection mechanism. These techniques aimed at both performance and endurance issues, but they all failed in providing applications a guaranteed performance. In this paper, we propose a real-time garbage-collection mechanism, which provides a guaranteed performance, for hard real-time systems. On the other hand, the proposed mechanism supports non-real-time tasks so that the potential bandwidth of the storage system can be fully utilized. A wear-leveling method, which is executed as a non-real-time service, is presented to resolve the endurance problem of flash memory. The capability of the proposed mechanism is demonstrated by a series of experiments over our system prototype.
Optimization of the internal grid of an offshore wind farm using Genetic algorithm
(2013)

Offshore wind energy is a promising solution thanks to its best performance of production. However its development leads to many technical and especially economic challenges among them the electrical grid topology attracts a large investment. In this paper, our objective is to minimize a part of this total investment which represents the initial cost of the middle voltage cables in the internal network with different cross sections. An approach based on Genetic Algorithm is developed to find the best topology to connect all wind turbines and substations. The proposed model initially accepts all possible configurations: radial, star, ring, and tree. The results prove that the optimization model can be used for designing the electrical architecture of the internal network of an offshore wind farm.
A flexible and low-cost tactile sensor for robotic applications
(2017)

For humans, the sense of touch is essential for interactions with the environment. With robots slowly starting to emerge as a human-centric technology, tactile information becomes increasingly important. Tactile sensors enable robots to gain information about contacts with the environment, which is required for safe interaction with humans or tactile exploration. Many sensor designs for the application on robots have been presented in literature so far. However, most of them are complex in their design and require high-tech tools for their manufacturing. In this paper, we present a novel design for a tactile sensor that can be built with low-cost, widely available materials, and low effort. The sensor is flexible, may be cut to arbitrary shapes and may have a customized spatial resolution. Both pressure distribution and absolute pressure on the sensor are detected. An experimental evaluation of our design shows low detection thresholds as well as high sensor accuracy. We seek to accelerate research on tactile feedback methods with this easy to replicate design. We consider our design a starting point for the integration of multiple sensor units to a large-scale tactile skin for robots.
A Dictionary-based Approach to Racism Detection in Dutch Social Media
(2016)

We present a dictionary-based approach to racism detection in Dutch social media comments, which were retrieved from tw o public Belgian social media sites likely to attract racist reactio ns. These comments were labeled as racist or non-racist by mu ltiple annotators. For our approach, three discourse dictionaries were create d: first, we created a dictionary by retrieving possibly raci st and more neutral terms from the training data, and then augmenting these with more general words to remove some bias. A second dictionary w s created through automatic expansion using a word2vec model trained on a large corpus of general Dutch text. Finall y, third dictionary was created by manually filtering out incorrect expansions. We trained multiple Support Vector Machines, using the dist ribu ion of words over the different categories in the dictionaries as f eatures. The best-performing model used the manually clean ed dictionary and obtained an F-score of 0.46 for the racist class on a test s et consisting of unseen Dutch comments, retrieved from the s ame sites used for the training set. The automated expansion of the dic tionary only slightly boosted the model’s performance, and this increase in performance was not statistically significant. The fact t hat the coverage of the expanded dictionaries did increase i ndicates that the words that were automatically added did occur in the corpus, b t were not able to meaningfully impact performance. The di ctionaries, code, and the procedure for requesting the corpus are availa ble at:https://github.com/clips/hades.
Online anomaly detection with concept drift adaptation using recurrent neural networks
(2018)

Anomaly detection in time series is an important task with several practical applications. The common approach of training one model in an offline manner using historical data is likely to fail under dynamically changing and non-stationary environments where the definition of normal behavior changes over time making the model irrelevant and ineffective. In this paper, we describe a temporal model based on Recurrent Neural Networks (RNNs) for time series anomaly detection to address challenges posed by sudden or regular changes in normal behavior. The model is trained incrementally as new data becomes available, and is capable of adapting to the changes in the data distribution. RNN is used to make multi-step predictions of the time series, and the prediction errors are used to update the RNN model as well as detect anomalies and change points. Large prediction error is used to indicate anomalous behavior or a change (drift) in normal behavior. Further, the prediction errors are also used to update the RNN model in such a way that short term anomalies or outliers do not lead to a drastic change in the model parameters whereas high prediction errors over a period of time lead to significant updates in the model parameters such that the model rapidly adapts to the new norm. We demonstrate the efficacy of the proposed approach on a diverse set of synthetic, publicly available and proprietary real-world datasets.
Towards a taxonomy of intrusion-detection systems
(1999)

Intrusion-detection systems aim at detecting attacks against computer systems and networks, or against information systems in general, as it is difficult to provide provably secure information systems and maintain them in such a secure state for their entire lifetime and for every utilization. Sometimes, legacy or operational constraints do not even allow a fully secure information system to be realized at all. Therefore, the task of intrusion-detection systems is to monitor the usage of such systems and to detect the apparition of insecure states. They detect attempts and active misuse by legitimate users of the information systems or external parties to abuse their privileges or exploit security vulnerabilities. In this paper, we introduce a taxonomy of intrusion-detection systems that highlights the various aspects of this area. This taxonomy defines families of intrusion-detection systems according to their properties. It is illustrated by numerous examples from past and current projects. q 1999 Elsevier Science B.V. All rights reserved.
Artificial immune systems - a new computational intelligence paradigm
(2002)

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